Achieving Reliability in Master-Worker Computing via Evolutionary Dynamics

We consider Internet-based Master-Worker computations, like SETI@home, where a master process sends tasks, across the Internet, to worker processes; workers execute and report back some result. However, these workers are not trustworthy and it might be at their best interest to report incorrect results. In such master-worker computations, the behavior and the best interest of the workers might change over time. We model such computations using evolutionary dynamics and we study the conditions under which the master can reliably obtain tasks results. In particular, we develop and analyze an algorithmic mechanism based on reinforcement learning to provide workers with the necessary incentives to eventually become truthful. Our analysis identifies the conditions under which truthful behavior can be ensured, and bounds the expected convergence time to that behavior. The analysis is complemented with simulations.